Abstract

In the last couple of years a new approach has been used with great success, called deep learning. Deep learning usually consists of neural networks with at least 3 layers, which are used to build hierarchical representations of data. Some of the deep learning methods are unsupervised and they are used to learn better features that are than used to solve a machine learning problem. Other deep learning methods are supervised and can be used directly to solve classification problems. Deep learning systems have been used to obtain the state of the art in different problems, such as object recognition in images or speech recognition, tested on standard datasets such as MNIST, CIFAR-10, Pascal VOC and TIMIT.